An alternative to transfer learning is multi-task learning [1]}, [2]}. Unlike transfer learning that addresses the source and target tasks sequentially, multi-task learning aims at learning multiple tasks simultaneously. It is based on the expectation that the tasks at hand share enough similarities such that learning them simultaneously with a single model would help learn more relevant features and lead to improved generalizability. Some studies have reported successful application of multi-task learning to medical image segmentation [3]}, [4]}. However, the effect of multi-task learning on the prediction calibration of the trained model has not been explored in previous studies.
